Short-revisit Synthetic Aperture Radar (SAR) provides a large amount of data for interferometric SAR (InSAR) application. Sequential Estimator can efficiently utilize the newly acquired SAR images to dynamically monitor surface deformation without reprocessing those already processed images. Sequential Estimator uses statistically homogeneous pixels (SHPs) to improve the phase quality. However, the selection of SHPs mainly focuses on SAR images acquired before initial processing, without considering the potential failure of SHPs in the newly acquired SAR images. In fact, the destruction of the homogeneity of SHPs in newly acquired data is a common occurrence, especially in some changed areas, such as the urban construction and the seasonal changes in farmland. The inaccurate identification of the SHPs can compromise the quality of phase filtering, ultimately affecting the precision of deformation results. In this paper, we proposed a novel SHP selection algorithm named SHP update algorithm (SHPU). SHPU detects the SHPs whose homogeneity gets destroyed in the new acquisitions and updates those SHPs. Experimental results demonstrate that SHPU can detect approximately over 70% of incorrect SHPs and reselect them. The required time of SHPU is only 30% of the time needed for re-estimation using newly acquired data.

The interferometric synthetic aperture radar (InSAR) image shift measurement technique is of great significance in processing high-precision digital elevation model (DEM) generation and deformation measurements. It can be used in steps such as image fine coregistration, interferometric phase unwrapping and absolute phase calibration in the InSAR processing flow without an external DEM. However, the shifts estimated by current methods are of low resolution and have high measurement noise, which may have adverse impacts on subsequent applications. In this paper, a lightweight, high-resolution and low-noise interferometric stereo-radargrammetric shift estimation network (SSENet) is proposed to solve the aforementioned problems. It introduces deep learning technology to the InSAR shift estimation task for the first time. We propose forming multiscale 3D coherence coefficient cubes by projecting the shift values of the images onto the third dimension and then using a 3D convolutional network for multiscale fusion and encoding, followed by decoding with linear layers. In addition, a dataset generation and augmentation scheme based on real data is designed for model training and evaluation. Several sets of real SAR images from different regions of the world were used to evaluate SSENet. Compared with the typical coherent cross-correlation approach, SSENet reduces the mean absolute error of the estimated shifts by approximately 79% while improving the resolution by a factor of 4×4, making it possible to restore the absolute interferometric phase. Finally, we demonstrate a stitching strategy for processing large-scale SAR images and discuss the multiple potential uses of SSENet in the InSAR processing chain.

Ionospheric effects constitute a key error source limiting the accuracy of surface deformation monitoring using L-band interferometric synthetic aperture radar (InSAR). Efficient identification of interferometric pairs affected by ionospheric disturbances is therefore essential for large-scale and high-throughput automated InSAR processing. To address this issue, a parameterized ionospheric detection method based on azimuth offsets derived from sub-aperture images is proposed. The proposed method integrates random-sampling pixel offset tracking (RS-POT) with piecewise Gaussian fitting to enable rapid and robust detection of ionospheric disturbances. Experimental validation was conducted using 50 interferometric pairs acquired by the LuTan-1 (LT-1) satellite, China’s first dual-satellite L-band SAR mission, covering high-, mid-, and low-latitude regions with varying ionospheric conditions. The results demonstrate that the proposed method can reliably identify ionospheric disturbances under diverse conditions while maintaining high computational efficiency. The proposed framework provides an effective solution for determining whether ionospheric correction is required, thereby improving the efficiency of automated interferometric processing workflows.

The Sentinel-1 (S1) satellites, operating at C-band in Terrain Observation by Progressive Scans (TOPS) mode, provide unprecedented opportunities for measuring large-scale tectonic motions using Interferometric Synthetic Aperture Radar (InSAR). However, the azimuth-varying Doppler centroid imposes stringent coregistration requirements, for which the Enhanced Spectral Diversity (ESD) method is typically employed to achieve the required high-precision azimuth coregistration. In practical applications, however, the ESD method is inevitably affected by various disturbances, especially by pronounced large-scale and non-stationary azimuth deformation. Such disturbances not only degrade the coregistration accuracy of the ESD method but also cause phase jumps at burst edges, thereby complicating the subsequent phase unwrapping. To address these issues, this study proposes an improved coregistration strategy that integrates the Local Iterative Pixel Offset Tracking (LI-POT) and Weighted ESD (WESD) to mitigate the effects of azimuth deformation and enhance the robustness of the ESD method. Furthermore, the Coherence Gradient Consistency (CGC) indicator is introduced to embed the proposed method into the standard processing workflow, enabling automated selection of appropriate coregistration strategies. The effectiveness of the proposed method was validated using S1 TOPS repeat-pass data acquired over four representative regions. By applying the proposed method, the phase jumps observed in the conventional processing were effectively corrected, demonstrating its superior accuracy and stability in complex deformation scenarios.

Distributed Scatterer Interferometric Synthetic Aperture Radar (DSInSAR) technology has been widely applied in areas with complex terrain and dense vegetation. However, DSInSAR is computationally intensive and requires considerable processing time. When new observations become available, the entire dataset must be reprocessed without utilizing previously obtained results. This makes DSInSAR unsuitable for long-term continuous monitoring. The Sequential Estimator partitions large datasets into fixed-size subsets and compresses these subsets to avoid redundant processing. The Recursive Sequential Estimator with Flexible Batches (RSEFB) method was proposed to partition large datasets into flexibly sized subsets. However, how to determine appropriate grouping boundaries remains unresolved. In this paper, a Change-Point-Based Deformation Grouping Strategy (CPDGS) is proposed to enhance the deformation estimation accuracy within each group, thereby reducing the attenuation of abrupt deformation signals during estimation. In the proposed method, a Bidirectional Long Short-Term Memory (Bidirectional LSTM) network is employed to identify the potential presence of deformation change points. Bayesian Estimator of Abrupt change, Seasonality and Trend (BEAST) is subsequently used to localize the change points. Considering computational efficiency, an upper limit is also set on the number of Single Look Complex (SLC) per group. Due to the lack of ground truth, simulated data were used for the network training. Comparative experiments show that the proposed Bidirectional LSTM achieves the best overall performance, with an accuracy of 88.43%, a precision of 90.76%, a recall of 86.04%, and an F1-score of 88.34%, outperforming the LSTM and Transformer models. Further comparisons with conventional change point detection methods show that the proposed method achieves an F1-score of 91.23%, higher than Cumulative Sum (CUSUM; 69.20%) and and Bayesian Online Change Point Detection (BOCPD; 84.44%). Experiments using real Interferometric Synthetic Aperture Radar (InSAR) deformation data further demonstrate its effectiveness in identifying deformation change points in practical scenarios.